Mokken Scaling analysis of Revised Clinical Interview Schedule (CIS-R) psychiatric status rating scales in a nationally representative sample: the 2007 Adult Psychiatric Morbidity Survey of England

This study aims to investigate double monotonicity of Revised Clinical Interview Schedule (CIS-R) psychiatric status rating scales data from 2007 Adult Psychiatric Morbidity Survey (APMS) within the framework of Mokken models. Results show that the items of the scale are sufficiently unidimensional in the general population for the CIS-R responses to be scalable according to broad Mokken principles. These do not require recourse to the parametric models for item response function curves typical of most applications of IRT in patient reported outcome measures research (PROMs). Our illustrative results provide an exemplar of the method. The methods are however more widely relevant for phenotype work in clinical and behavioural research, and so should appeal to those who work on addictions or in clinical medicine. Central Stochl and Croudace (2014) Email: J Addict Med Ther 2(1): 1005 (2014) 2/5 (formerly the Office of Population Census and Surveys) and also by clinical trialists to measure baseline psychiatric symptoms (usually for anxious/depressive morbidity). The CIS–R enables the generation of ICD–10 diagnoses and as the authors indicate: “...unusually, the CIS–R includes a comprehensive assessment of anxiety and depressive symptoms regardless of whether the respondent meets criteria for a specific disorder. It is therefore particularly well suited for exploration of sub-threshold conditions such as mixed anxiety and depressive disorder” [1]. Their aim was to seek evidence for a notion of mixed anxiety and depressive disorder (MADD) based on clinical information on signs and symptoms initially used to make DSM diagnoses. MADD was described by the authors (in the manner of a hypothesis) as both “a provisional diagnosis in ICD-10 and DSMIV”, and involving “the presence of both anxiety and depressive symptoms”. Our motivation is more methodological and is oriented from a “psychometric epidemiology” perspective. Using data from the same survey (the third in the series, conducted in 2007) we sought to evaluate the strength of these psychiatric status measures in terms of a simple psychometric scaling model, for measuring the severity of common mental disorder symptoms (morbidity). We do not consider any of the screening questionnaires that were also included, since they do not offer clinical ratings, but comprise traditional self report questionnaire screens e. g. for alcohol. The APMS survey relied on the CIS-R to assess common mental disorder in the first (“phase one”) interview. The APMS reports used CIS-R data as the basis for diagnosis, based on the range of non-psychotic symptoms that it assesses (over the period of the past week). This use of the CIS-R is typical, with responses used to generate both an overall score and for diagnoses (after application of relevant clinical algorithms). Our analysis relates to the overall scoring approach. We sought to operationalise a dimension of anxiety and depressive morbidity severity. This involves the idea of a single dimension for the CIS-R symptoms, as well as scaling criteria. Following Das-Munshi we binary scored the CIS-R psychopathological data and evaluated whether the symptoms endorsed at >1 score thresholds form a single unidimensional continuum, when evaluated using non-parametric item response theory, by performing a Mokken scaling analysis. Our analysis embraces the utility of a simple, coarsened scaling approach to the latent ordering of survey members into strata of increasing likelihood of the presence of common mental disorder.